Overview

Dataset statistics

Number of variables15
Number of observations314
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.2 KiB
Average record size in memory128.0 B

Variable types

Numeric13
Categorical1
DateTime1

Warnings

YEAR is highly correlated with formatted_dateHigh correlation
PM_RETIRO is highly correlated with PM_CIUDADLINEALHigh correlation
PM_CIUDADLINEAL is highly correlated with PM_RETIROHigh correlation
DEW_POINT is highly correlated with TEMPERATUREHigh correlation
TEMPERATURE is highly correlated with DEW_POINTHigh correlation
formatted_date is highly correlated with YEARHigh correlation
TEMPERATURE has unique values Unique
WIND_SPEED has unique values Unique
formatted_date has unique values Unique
PM_RETIRO has 158 (50.3%) zeros Zeros
PM_VALLECAS has 165 (52.5%) zeros Zeros
PM_CIUDADLINEAL has 158 (50.3%) zeros Zeros
COMMULATIVE_PRECIPITATION has 130 (41.4%) zeros Zeros

Reproduction

Analysis started2021-05-04 16:43:03.863674
Analysis finished2021-05-04 16:43:39.519536
Duration35.66 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

YEAR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.5
Minimum2010
Maximum2015
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:43:39.616277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12011
median2012.5
Q32014
95-th percentile2015
Maximum2015
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.716765664
Coefficient of variation (CV)0.0008530512616
Kurtosis-1.276741608
Mean2012.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum631925
Variance2.947284345
MonotocityIncreasing
2021-05-04T11:43:39.840909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
201553
16.9%
201053
16.9%
201452
16.6%
201352
16.6%
201252
16.6%
201152
16.6%
ValueCountFrequency (%)
201053
16.9%
201152
16.6%
201252
16.6%
201352
16.6%
201452
16.6%
ValueCountFrequency (%)
201553
16.9%
201452
16.6%
201352
16.6%
201252
16.6%
201152
16.6%

Week
Real number (ℝ≥0)

Distinct53
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.66878981
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:43:40.050866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median27
Q340
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.13238219
Coefficient of variation (CV)0.5674191556
Kurtosis-1.19964865
Mean26.66878981
Median Absolute Deviation (MAD)13
Skewness0.0008578062142
Sum8374
Variance228.9889909
MonotocityNot monotonic
2021-05-04T11:43:40.298239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
276
 
1.9%
266
 
1.9%
246
 
1.9%
236
 
1.9%
226
 
1.9%
216
 
1.9%
206
 
1.9%
196
 
1.9%
186
 
1.9%
176
 
1.9%
Other values (43)254
80.9%
ValueCountFrequency (%)
16
1.9%
26
1.9%
36
1.9%
46
1.9%
56
1.9%
ValueCountFrequency (%)
532
 
0.6%
526
1.9%
516
1.9%
506
1.9%
496
1.9%

SEASON
Categorical

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
1
84 
2
79 
3
78 
4
73 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters314
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4
ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%
2021-05-04T11:43:40.750994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-04T11:43:40.906087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%

Most occurring characters

ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number314
100.0%

Most frequent character per category

ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%

Most occurring scripts

ValueCountFrequency (%)
Common314
100.0%

Most frequent character per script

ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII314
100.0%

Most frequent character per block

ValueCountFrequency (%)
184
26.8%
279
25.2%
378
24.8%
473
23.2%

PM_RETIRO
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct157
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.46563526
Minimum0
Maximum266.6325301
Zeros158
Zeros (%)50.3%
Memory size4.9 KiB
2021-05-04T11:43:41.115440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q379.67566708
95-th percentile152.2670683
Maximum266.6325301
Range266.6325301
Interquartile range (IQR)79.67566708

Descriptive statistics

Standard deviation53.73795679
Coefficient of variation (CV)1.20852781
Kurtosis0.4482246595
Mean44.46563526
Median Absolute Deviation (MAD)0
Skewness1.028076347
Sum13962.20947
Variance2887.768
MonotocityNot monotonic
2021-05-04T11:43:41.346854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0158
50.3%
68.323353291
 
0.3%
38.744047621
 
0.3%
48.187878791
 
0.3%
101.53012051
 
0.3%
91.624242421
 
0.3%
139.90526321
 
0.3%
99.160714291
 
0.3%
99.212121211
 
0.3%
24.187878791
 
0.3%
Other values (147)147
46.8%
ValueCountFrequency (%)
0158
50.3%
18.660714291
 
0.3%
22.952380951
 
0.3%
24.187878791
 
0.3%
26.048192771
 
0.3%
ValueCountFrequency (%)
266.63253011
0.3%
214.43902441
0.3%
194.40993791
0.3%
176.21604941
0.3%
175.68452381
0.3%

PM_VALLECAS
Real number (ℝ≥0)

ZEROS

Distinct150
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.22140165
Minimum0
Maximum248.3115942
Zeros165
Zeros (%)52.5%
Memory size4.9 KiB
2021-05-04T11:43:41.572759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q377.80208485
95-th percentile167.8439024
Maximum248.3115942
Range248.3115942
Interquartile range (IQR)77.80208485

Descriptive statistics

Standard deviation55.38134783
Coefficient of variation (CV)1.281340857
Kurtosis0.5870552698
Mean43.22140165
Median Absolute Deviation (MAD)0
Skewness1.146887486
Sum13571.52012
Variance3067.093688
MonotocityNot monotonic
2021-05-04T11:43:41.851828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0165
52.5%
194.40119761
 
0.3%
53.39024391
 
0.3%
130.36690651
 
0.3%
10.454545451
 
0.3%
248.31159421
 
0.3%
75.21
 
0.3%
106.81
 
0.3%
46.819277111
 
0.3%
108.45783131
 
0.3%
Other values (140)140
44.6%
ValueCountFrequency (%)
0165
52.5%
61
 
0.3%
9.3333333331
 
0.3%
10.454545451
 
0.3%
13.021
 
0.3%
ValueCountFrequency (%)
248.31159421
0.3%
2081
0.3%
197.65060241
0.3%
196.5950921
0.3%
195.04729731
0.3%

PM_CIUDADLINEAL
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct157
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.26967966
Minimum0
Maximum249.6319018
Zeros158
Zeros (%)50.3%
Memory size4.9 KiB
2021-05-04T11:43:42.198902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q376.76964286
95-th percentile159.803912
Maximum249.6319018
Range249.6319018
Interquartile range (IQR)76.76964286

Descriptive statistics

Standard deviation54.09444422
Coefficient of variation (CV)1.221929877
Kurtosis0.3846405306
Mean44.26967966
Median Absolute Deviation (MAD)0
Skewness1.058025743
Sum13900.67941
Variance2926.208896
MonotocityNot monotonic
2021-05-04T11:43:42.540987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0158
50.3%
82.764705881
 
0.3%
78.251497011
 
0.3%
45.542682931
 
0.3%
73.542168671
 
0.3%
55.777108431
 
0.3%
159.39156631
 
0.3%
174.80733941
 
0.3%
104.66071431
 
0.3%
90.237804881
 
0.3%
Other values (147)147
46.8%
ValueCountFrequency (%)
0158
50.3%
13.77976191
 
0.3%
17.722891571
 
0.3%
23.207142861
 
0.3%
28.546583851
 
0.3%
ValueCountFrequency (%)
249.63190181
0.3%
208.15151521
0.3%
194.16770191
0.3%
186.87730061
0.3%
183.21084341
0.3%

PM_CENTRO
Real number (ℝ≥0)

Distinct311
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.21641491
Minimum17.73214286
Maximum271.8433735
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:43:42.898032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum17.73214286
5-th percentile36.55089286
Q166.08725163
median86.35825119
Q3118.6997166
95-th percentile182.0143933
Maximum271.8433735
Range254.1112306
Interquartile range (IQR)52.61246501

Descriptive statistics

Standard deviation45.41258061
Coefficient of variation (CV)0.4719837115
Kurtosis1.644776369
Mean96.21641491
Median Absolute Deviation (MAD)25.02874242
Skewness1.154750541
Sum30211.95428
Variance2062.302478
MonotocityNot monotonic
2021-05-04T11:43:43.171811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.458333332
 
0.6%
83.255952382
 
0.6%
70.833333332
 
0.6%
115.90277781
 
0.3%
105.86144581
 
0.3%
126.0059881
 
0.3%
80.017964071
 
0.3%
61.814814811
 
0.3%
70.269461081
 
0.3%
68.208333331
 
0.3%
Other values (301)301
95.9%
ValueCountFrequency (%)
17.732142861
0.3%
21.511904761
0.3%
23.916666671
0.3%
25.782894741
0.3%
29.185628741
0.3%
ValueCountFrequency (%)
271.84337351
0.3%
2661
0.3%
254.40476191
0.3%
242.93452381
0.3%
234.3273811
0.3%

DEW_POINT
Real number (ℝ)

HIGH CORRELATION

Distinct311
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.029029838
Minimum-21.68229167
Maximum23.61904762
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:43:43.405501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-21.68229167
5-th percentile-18.67529762
Q1-10.11309524
median2.74702381
Q314.74553571
95-th percentile21.37113095
Maximum23.61904762
Range45.30133929
Interquartile range (IQR)24.85863095

Descriptive statistics

Standard deviation13.383303
Coefficient of variation (CV)6.595912364
Kurtosis-1.344081318
Mean2.029029838
Median Absolute Deviation (MAD)12.31845238
Skewness-0.04594958744
Sum637.1153693
Variance179.1127991
MonotocityNot monotonic
2021-05-04T11:43:43.611949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18.083333332
 
0.6%
-1.3752
 
0.6%
17.47023812
 
0.6%
-1.9821428571
 
0.3%
7.3035714291
 
0.3%
-19.541666671
 
0.3%
-6.5238095241
 
0.3%
-19.458333331
 
0.3%
-9.2023809521
 
0.3%
19.946428571
 
0.3%
Other values (301)301
95.9%
ValueCountFrequency (%)
-21.682291671
0.3%
-21.184523811
0.3%
-20.898809521
0.3%
-20.821428571
0.3%
-20.160714291
0.3%
ValueCountFrequency (%)
23.619047621
0.3%
23.476190481
0.3%
23.3751
0.3%
23.244047621
0.3%
23.130952381
0.3%

HUMIDITY
Real number (ℝ≥0)

Distinct311
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.55365172
Minimum0
Maximum97.22916667
Zeros1
Zeros (%)0.3%
Memory size4.9 KiB
2021-05-04T11:43:43.836318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.27797619
Q142.4047619
median55.29464286
Q365.90327381
95-th percentile79.2014881
Maximum97.22916667
Range97.22916667
Interquartile range (IQR)23.4985119

Descriptive statistics

Standard deviation15.91684377
Coefficient of variation (CV)0.2917649556
Kurtosis-0.4834138936
Mean54.55365172
Median Absolute Deviation (MAD)12.14880952
Skewness-0.1677940946
Sum17129.84664
Variance253.3459157
MonotocityNot monotonic
2021-05-04T11:43:44.074712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.40476192
 
0.6%
50.988095242
 
0.6%
65.886904762
 
0.6%
68.160714291
 
0.3%
30.7656251
 
0.3%
77.267857141
 
0.3%
33.613095241
 
0.3%
64.517857141
 
0.3%
85.511904761
 
0.3%
69.696428571
 
0.3%
Other values (301)301
95.9%
ValueCountFrequency (%)
01
0.3%
18.34523811
0.3%
21.440476191
0.3%
21.505952381
0.3%
22.005952381
0.3%
ValueCountFrequency (%)
97.229166671
0.3%
85.511904761
0.3%
84.880952381
0.3%
83.434523811
0.3%
82.797619051
0.3%

PREASSURE
Real number (ℝ≥0)

Distinct306
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1013.289801
Minimum0
Maximum1036.470238
Zeros1
Zeros (%)0.3%
Memory size4.9 KiB
2021-05-04T11:43:44.386367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1001.956845
Q11007.831845
median1016.955357
Q31023.982143
95-th percentile1030.81369
Maximum1036.470238
Range1036.470238
Interquartile range (IQR)16.15029762

Descriptive statistics

Standard deviation58.11599363
Coefficient of variation (CV)0.05735377338
Kurtosis297.9494666
Mean1013.289801
Median Absolute Deviation (MAD)7.916666667
Skewness-17.03856178
Sum318172.9976
Variance3377.468716
MonotocityNot monotonic
2021-05-04T11:43:44.620741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000.9642862
 
0.6%
1023.9821432
 
0.6%
1018.4583332
 
0.6%
1022.5595242
 
0.6%
1029.6252
 
0.6%
1004.8752
 
0.6%
1018.752
 
0.6%
1011.8571432
 
0.6%
1002.2023811
 
0.3%
1003.7976191
 
0.3%
Other values (296)296
94.3%
ValueCountFrequency (%)
01
0.3%
997.26785711
0.3%
1000.4166671
0.3%
1000.5476191
0.3%
1000.6845241
0.3%
ValueCountFrequency (%)
1036.4702381
0.3%
1034.0476191
0.3%
1033.6354171
0.3%
1033.3541671
0.3%
1032.9464291
0.3%

TEMPERATURE
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.84754088
Minimum-1.862217018
Maximum31.47111632
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:43:44.857648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.862217018
5-th percentile3.592414374
Q18.428815379
median19.38095238
Q326.88729508
95-th percentile30.03449454
Maximum31.47111632
Range33.33333333
Interquartile range (IQR)18.4584797

Descriptive statistics

Standard deviation9.282351052
Coefficient of variation (CV)0.5200913177
Kurtosis-1.389319215
Mean17.84754088
Median Absolute Deviation (MAD)8.62363388
Skewness-0.20254832
Sum5604.127835
Variance86.16204105
MonotocityNot monotonic
2021-05-04T11:43:45.074072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.707259951
 
0.3%
6.3930523031
 
0.3%
27.64597971
 
0.3%
24.51854021
 
0.3%
26.714090551
 
0.3%
5.0659640911
 
0.3%
8.6178766591
 
0.3%
7.4859484781
 
0.3%
29.109679941
 
0.3%
24.333138171
 
0.3%
Other values (304)304
96.8%
ValueCountFrequency (%)
-1.8622170181
0.3%
0.15768930521
0.3%
1.2042349731
0.3%
1.9954462661
0.3%
2.3239656521
0.3%
ValueCountFrequency (%)
31.471116321
0.3%
31.114949261
0.3%
30.958821231
0.3%
30.934426231
0.3%
30.833807791
0.3%

WIND_SPEED
Real number (ℝ≥0)

UNIQUE

Distinct314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.34791701
Minimum3.302202381
Maximum177.1026667
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:43:45.326364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.302202381
5-th percentile5.621282738
Q19.954895833
median15.47050595
Q327.25004464
95-th percentile65.0183404
Maximum177.1026667
Range173.8004643
Interquartile range (IQR)17.29514881

Descriptive statistics

Standard deviation23.12628606
Coefficient of variation (CV)0.9905074637
Kurtosis11.44095404
Mean23.34791701
Median Absolute Deviation (MAD)6.978541667
Skewness2.936798241
Sum7331.24594
Variance534.8251067
MonotocityNot monotonic
2021-05-04T11:43:45.531846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.260416671
 
0.3%
12.311964291
 
0.3%
22.38251
 
0.3%
20.628154761
 
0.3%
12.241904761
 
0.3%
10.736190481
 
0.3%
66.626607141
 
0.3%
10.462023811
 
0.3%
4.9999404761
 
0.3%
9.9175595241
 
0.3%
Other values (304)304
96.8%
ValueCountFrequency (%)
3.3022023811
0.3%
3.7068263471
0.3%
4.2002380951
0.3%
4.2195833331
0.3%
4.2651190481
0.3%
ValueCountFrequency (%)
177.10266671
0.3%
140.52690481
0.3%
138.55857141
0.3%
115.41803571
0.3%
110.73119051
0.3%

COMMULATIVE_PRECIPITATION
Real number (ℝ≥0)

ZEROS

Distinct127
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3192.679299
Minimum0
Maximum999990
Zeros130
Zeros (%)41.4%
Memory size4.9 KiB
2021-05-04T11:43:45.787641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.8
Q310.05
95-th percentile39.05
Maximum999990
Range999990
Interquartile range (IQR)10.05

Descriptive statistics

Standard deviation56432.25065
Coefficient of variation (CV)17.67551494
Kurtosis313.9999333
Mean3192.679299
Median Absolute Deviation (MAD)0.8
Skewness17.72004233
Sum1002501.3
Variance3184598913
MonotocityNot monotonic
2021-05-04T11:43:46.101217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0130
41.4%
0.110
 
3.2%
0.75
 
1.6%
0.25
 
1.6%
4.94
 
1.3%
3.14
 
1.3%
0.84
 
1.3%
3.44
 
1.3%
10.13
 
1.0%
1.13
 
1.0%
Other values (117)142
45.2%
ValueCountFrequency (%)
0130
41.4%
0.110
 
3.2%
0.25
 
1.6%
0.31
 
0.3%
0.42
 
0.6%
ValueCountFrequency (%)
9999901
0.3%
2231
0.3%
102.31
0.3%
75.81
0.3%
74.11
0.3%

formatted_date
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012766.688
Minimum2010010
Maximum2015530
Zeros0
Zeros (%)0.0%
Memory size4.9 KiB
2021-05-04T11:43:46.397813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2010010
5-th percentile2010166.5
Q12011262.5
median2012765
Q32014267.5
95-th percentile2015373.5
Maximum2015530
Range5520
Interquartile range (IQR)3005

Descriptive statistics

Standard deviation1723.421958
Coefficient of variation (CV)0.0008562452708
Kurtosis-1.256219614
Mean2012766.688
Median Absolute Deviation (MAD)1505
Skewness0.003308513388
Sum632008740
Variance2970183.244
MonotocityStrictly increasing
2021-05-04T11:43:46.647940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20101101
 
0.3%
20152501
 
0.3%
20112901
 
0.3%
20133401
 
0.3%
20102701
 
0.3%
20150601
 
0.3%
20123201
 
0.3%
20143701
 
0.3%
20113001
 
0.3%
20133501
 
0.3%
Other values (304)304
96.8%
ValueCountFrequency (%)
20100101
0.3%
20100201
0.3%
20100301
0.3%
20100401
0.3%
20100501
0.3%
ValueCountFrequency (%)
20155301
0.3%
20155201
0.3%
20155101
0.3%
20155001
0.3%
20154901
0.3%
Distinct313
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
Minimum2010-01-10 00:00:00
Maximum2016-01-10 00:00:00
2021-05-04T11:43:46.890270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:47.145562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2021-05-04T11:43:04.635121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:04.844561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:05.038052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:05.243502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:05.444861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:05.636154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:05.832387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:06.021878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:06.237304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:06.434777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:06.674135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:06.882633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:07.085099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:07.314629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:07.506650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:07.701105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:07.896584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:08.078133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:08.271581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:08.466102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:08.659803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:08.861802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:09.085805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:09.288772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:09.512175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:09.738576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:10.022810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:10.354868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:10.677008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:10.935832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:11.451352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:11.642872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:11.881201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:12.072724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:12.313564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:12.511322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:12.719775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:12.907798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:13.088028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:13.308950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:13.512404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:13.704891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:13.887433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:14.069435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:14.270472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:14.468947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:14.682828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:14.886314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:15.084753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:15.282225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:15.472717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:15.668746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:15.866181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:16.068679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:16.282223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:16.479661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:16.785844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:17.064099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:17.274536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:17.474035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:17.742285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:17.942755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:18.140090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:18.316817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:18.500358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:18.686827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:18.881308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:19.063850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:19.245335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:19.437860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:19.637691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:19.838686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:20.039391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:20.226858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:20.400394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:20.578917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:20.763424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:20.953492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:21.143952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:21.327686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:21.506043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:21.697563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:21.905973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:22.141344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:22.442538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:22.637027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:22.834755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:23.022761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:23.218754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:23.404225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:23.586738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:23.774236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:23.969713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:24.186942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:24.392392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:24.608029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:24.834024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:25.031041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:25.209549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:25.433936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:25.652352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:25.951582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:26.237827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:26.514856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:26.794630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:27.017036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:27.249416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:27.468828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:27.674298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:27.878264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:28.085719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:28.291878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:28.486356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:28.688817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:28.953109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:29.183493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:29.379599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:29.587044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:29.793744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:30.001950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:30.212386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:30.455767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:30.658226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:31.250651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:31.494038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:31.706471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:31.929873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:32.141344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:32.360276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:32.613111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:32.822556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:33.048909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:33.277842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:33.477277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:33.672755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:33.878237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:34.093215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:34.312202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:34.497714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:34.688712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:34.894673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:35.101122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:35.305579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:35.519004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:35.731467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:35.941911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:36.153930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:36.364838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:36.562330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:36.766783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:36.968245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:37.198630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:37.471409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:37.823978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:38.049797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-04T11:43:38.279009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-04T11:43:47.411849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-04T11:43:47.910013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-04T11:43:48.461193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-04T11:43:48.922958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-04T11:43:38.657358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-04T11:43:39.243758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

YEARWeekSEASONPM_RETIROPM_VALLECASPM_CIUDADLINEALPM_CENTRODEW_POINTHUMIDITYPREASSURETEMPERATUREWIND_SPEEDCOMMULATIVE_PRECIPITATIONformatted_dateYearWeek
02010140.00.00.074.119048-19.97619050.6785711031.470238-1.86221732.7785710.020100102010-01-10
12010240.00.00.088.250000-18.08333349.7500001031.3452380.15768943.4644640.020100202010-01-17
22010340.00.00.0131.897059-14.20238146.4047621028.2559525.01717438.8438100.020100302010-01-24
32010440.00.00.063.413534-17.74404829.6845241023.7380957.00292751.7404170.020100402010-01-31
42010540.00.00.076.416667-14.01785752.9345241027.6250003.83645610.4060710.020100502010-02-07
52010640.00.00.080.017964-16.98214338.1309521029.4166675.14402814.1273210.020100602010-02-14
62010740.00.00.098.898810-16.13095236.3392861022.5595246.39305211.9607140.020100702010-02-21
72010840.00.00.0133.523810-5.49404863.1726191015.5059528.61787717.3730364.720100802010-02-28
82010910.00.00.094.410714-10.15476252.6071431025.4642867.48594824.8736315.120100902010-03-07
920101010.00.00.070.553571-10.55952449.1011901024.3392868.14949326.57178610.120101002010-03-14

Last rows

YEARWeekSEASONPM_RETIROPM_VALLECASPM_CIUDADLINEALPM_CENTRODEW_POINTHUMIDITYPREASSURETEMPERATUREWIND_SPEEDCOMMULATIVE_PRECIPITATIONformatted_dateYearWeek
304201544329.63750074.50000028.54658429.839286-3.04166750.1547621024.22023814.60929029.8995834.320154402015-11-08
305201545394.52381052.19354896.25595293.3511901.90476275.5041321025.19834712.83333317.471548999990.020154502015-11-15
3062015463175.684524208.000000183.210843175.9523814.8982040.0000000.00000013.0709733.7068260.020154602015-11-22
307201547357.36363670.64705959.29813760.4821430.57142997.2291671033.3541679.86202216.3892267.120154702015-11-29
3082015483140.5644176.000000148.794521135.416667-9.20238170.2500001031.9166673.98770520.5636310.020154802015-12-06
3092015493176.216049174.142857168.098765160.455090-7.70658759.7005991024.9520967.98115257.3051500.020154902015-12-13
3102015504170.718563193.800000179.491018169.714286-3.57738174.4107141027.9880958.4032015.0036310.020155002015-12-20
311201551490.79518113.02000097.02994097.454545-9.68452455.4047621029.7797627.08099177.8649400.720155102015-12-27
3122015524266.632530248.311594249.631902254.404762-6.52381076.7500001027.3452385.5392274.2002380.020155202016-01-03
3132015534139.905263181.055556150.473684154.229167-9.22916763.6875001031.6875005.47336112.6192710.020155302016-01-10